CDS&E: Harnessing Self-Organizing Maps for the Discovery of Star Formation in Molecular Clouds

CDS

基本信息

批准号：
2107942
负责人：
Stella Offner
金额：
$ 41.28万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107942&HistoricalAwards=false
关键词：
CDS&E Harnessing Self Organizing Maps

项目摘要

Stars like our Sun are born in large clouds of dust and gas that make thousands of stars at once. Astronomers observe these gas clouds to study young stars and learn how stars and planets form. These clouds are complex, and the observations are difficult to interpret. This project uses machine learning to address an important mystery in astronomy: Why do stars have a great variety of total mass? Stars are found to have a mass between a tenth the mass of our Sun and up to 100 times the mass of the Sun. The Investigators will use computer models of forming stars to test their method and will apply it to recent observations of star-forming regions. The team will develop presentations for the public and train undergraduates in the Texas Astronomy Undergraduate Research experience for Under-represented Students (TAURUS) summer program in machine learning methods. The investigators plan to combine state-of-the-art numerical simulations, unsupervised machine learning, and molecular line observations to study the gas engaged in the earliest stages of star formation within molecular clouds. The team will apply an unsupervised neural network algorithm, known as a Self-Organizing Map (SOM), to identify clusters comprising star-forming structures and the gas that forms them in observational data. As gas structures within molecular clouds are complex and do not have well-defined boundaries, this necessitates new techniques for SOM cluster inference. Molecular line “synthetic observations” will be produced and compared to the observational data to aid in interpretation and exaction of physical properties. This work will build bridges between astronomy, statistics and computer science and thereby advance new approaches for data segmentation and clustering that will be of broader use to the astronomy community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

像我们的太阳这样的恒星诞生于大量的灰尘和气体云中，一次造成数千星。天文学家观察这些气体云研究年轻的恒星并学习恒星和行星的形成方式。这些云很复杂，观察结果很难解释。该项目使用机器学习来解决天文学中的重要谜团：为什么恒星总质量有很多？发现恒星的质量是我们太阳的十分之一，最多是太阳质量的100倍。研究人员将使用成型恒星的计算机模型来测试其方法，并将其应用于最新的恒星形成区域。该团队将为公众开发演讲，并在德克萨斯州天文学的本科生研究经验中培训本科生，以涉及代表性不足的学生（Taurus）夏季计划的机器学习方法。研究人员计划结合最新的数值模拟，无监督的机器学习和分子线观测值，以研究分子云中恒星形成最早的气体。该团队将应用无监督的神经元网络算法，称为自组织地图（SOM），以识别完成恒星形成结构的群集，并在观察数据中形成它们的气体。由于分子云中的气体结构是复杂的，并且没有明确的边界，因此这需要用于SOM群集推断的新技术。将产生分子线“合成观察”，并将其与观察数据进行比较，以帮助解释和执行物理性质。这项工作将在天文学，统计学和计算机科学之间建造桥梁，从而推动新方法进行数据分割和聚类，这对天文学界将更广泛使用。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估值得支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Core Formation, Coherence and Collapse: Three Phases for Core Evolution

核心形成、凝聚和崩溃：核心演化的三个阶段

DOI：
发表时间：
2022
期刊：
Monthly notices of the Royal Astronomical Society
影响因子：
4.8
作者：
Offner, S.;Taylor, J.;Markey, C.;Chen, H.;Pineda, J.;Goodman, A.;Burkhert, A.;Ginsburg, A.;Choudhury, S.
通讯作者：
Choudhury, S.

Flow of gas detected from beyond the filaments to protostellar scales in Barnard 5

在巴纳德 5 号中检测到从细丝到原恒星鳞片的气体流动

DOI：
10.1051/0004-6361/202346357
发表时间：
2023
期刊：
Astronomy & Astrophysics
影响因子：
6.5
作者：
Valdivia-Mena, M.T.;Pineda, J.E.;Segura-Cox, D.M.;Caselli, P.;Schmiedeke, A.;Choudhury, S.;Offner, S.;Neri, R.;Goodman, A.;Fuller, G.
通讯作者：
Fuller, G.

Turbulence, coherence, and collapse: Three phases for core evolution

湍流、凝聚和崩溃：核心演化的三个阶段

DOI：
10.1093/mnras/stac2734
发表时间：
2022
期刊：
Monthly Notices of the Royal Astronomical Society
影响因子：
4.8
作者：
Offner, Stella S;Taylor, Josh;Markey, Carleen;Chen, Hope How-Huan;Pineda, Jaime E;Goodman, Alyssa A;Burkert, Andreas;Ginsburg, Adam;Choudhury, Spandan
通讯作者：
Choudhury, Spandan

Automating t-SNE Parameterization with Prototype-Based Learning of Manifold Connectivity

通过基于原型的流形连接学习实现 t-SNE 参数化自动化